Critical use of GenAI: Creation

Within education, outright creation is probably the most controversial use of LLMs.

After all, if we ask students to create something, we're usually interested in having them experience the brainstorming, the dead ends, the struggle of putting an idea to practice and the satisfaction of seeing things come together at the end of this (arduous) process. The learning is happening during the journey, so why take that away from students?

Truth is, there are no good reasons to do so, but there is a real risk that students self-sabotage and skip over the process itself. Their reasons will vary from self-doubt to time pressure to just not really enjoying the friction that comes with learning. As a teacher, you can counteract these tendencies by being upfront with students about the value of struggle during ideation. You can also make sure that you monitor or even assess their process, to put the incentives in the right places.

Alternatively, you can allow them to use LLMs for creation while emphasising critical thinking, which is the topic of this post. Building on the tentative framework for critical thinking about GenAI, here we will look at the creation of artefacts or ideas, two subcategories in Brachman's ontology of GenAI use cases (Brachman et al., 2024).

Artefacts

Artefacts are things that can be used directly or after some modification, such as research proposals, intervention strategies and prototype descriptions. The five-step process model of thinking, shown in figure 1, applies to the development of such artefacts, with all design choices being 'action selections', which are ideally consequences of a critical reflection process. The risk of GenAI use is that the action selection happens without such reflection, so in teaching you would have to ensure that students go through all the steps in a reflective, careful way.

Problem identification

This is an easy step to ignore, but it is key to thinking critically about artefact creation. Any artefact is supposed to meet a need and regardless of the assignment you give students, it is important for them to think about what the artefact is supposed to accomplish. This means:

• Understanding the problem that needs fixing
• Identifying what counts as solving the problem: when can you say an artefact is good?
• Conversely, identifying what failure would look like

Information gathering

Now that the problem for the artefact is known, students can explore what has already been done to tackle that problem.

• Which earlier attempts were made? What were they based on?
• How well did they work? What were their shortcomings?

Whether it's reading journal articles to prepare for a research proposal, comparing marketing strategies and exploring their rationales or putting technical specifications side by side, this step is an exploration stage that sets the proper frame for assessing novel solution to the problem at hand.

Sensemaking

In the sensemaking step, the students take a close look at the information they gathered and write down criteria for artefact quality, based on their findings. These evaluation criteria will ultimately be used to judge GenAI output, but also to properly prompt the LLM in the first place.

Belief formation

Belief formation is a refinement of the previous step, in which students take a stance on the relative importance of the different criteria. Here, they make clear decisions on particular features that should be present in the artefact, identifying which features are must-haves and which are nice-to-haves. In a course, this is a good moment for students to share their results and get feedback from you or from their peers.

Action selection

Only a this point can the students prompt a GenAI system to generate an artefact. Using their criteria, they can now prompt clearly about the requirements. More importantly for critical thinking, the students can now evaluate the GenAI output quality on the basis of their own benchmarks. This allows them to refine the artefact through iterative prompting or manual modification, while being explicitly aware of shortcomings.

Ideas

Ideas differ from artefacts in that they are insights that can give direction to a thought process or creative process. With artefact generation, the GenAI output, once critically assessed and properly revised, is the goal. When generating ideas, the GenAI output is often the start of something else.

Now, to critically assess the value of an idea, similar logic applies as was the case for artefacts. You need to have a good notion of what makes an idea a good idea before you can evaluate it. This is done by problem identification (what is the purpose of the idea?), information gathering (what relevant knowledge is out there?) and sensemaking (which knowledge should be part of the idea?). However, in the case of generating ideas it's already at the step of belief formation that students invoke GenAI, to brainstorm what should go between the broad knowledge they obtained and setting up a subsequent course of action.

To think critically here, the challenge for students will be to generate multiple ideas and pit them against each other using a systematic approach. This could again be benchmarks, but approaches like an impact/feasibility matrix might also work. In any case, the critical step will again be to decide whether the GenAI output is good for the purpose at hand.

Importance of preparation

As these two approaches show, critical thinking during creation with GenAI is strongly dependent on the work done before the prompt. The better the preparation, the deeper the considerations can be once GenAI starts spewing results. Teachers might opt to include an extra step in which students sketch an artefact or create an idea without GenAI first, so that they have some human ideation to add to their overall analysis.

In any case, critical thinking is unlikely to happen without due preparation, shifting the focus of teachers to ensuring a good process. As the next posts will show, the other categories in Brachman's ontology also require a more process-oriented mode of teaching.

References

Brachman, M., El-Ashry, A., Dugan, C., & Geyer, W. (2024, May). How knowledge workers use and want to use LLMs in an enterprise context. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (pp. 1-8).

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Critical use of GenAI: Advice

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